1. Field of the Invention
The present invention is related to generating scenarios of hydrocarbon reservoirs based on limited amount of information on a target hydrocarbon reservoir, and more particularly to automatically supplying missing parameters and an uncertainty associated with each supplied parameter allowing to valuating the target hydrocarbon reservoir.
2. Background Description
Determining whether investing in a new hydrocarbon reservoir candidate is a good business decision depends on the inherent value of the reservoir. Factors determining the inherent value of the reservoir include, for example, the total amount of material that is ultimately recoverable from each new hydrocarbon reservoir (production potential), market prices (oil and/or natural gas prices) and the cost of recovering that material, or capture difficulty. Until the material is actually recovered, however, that inherent value can be estimated from different, primarily known, reservoir properties.
However, investment opportunities frequently include a number of candidate reservoirs with spotty available information on each. Even with a dearth of available information, however, it is imperative to assess the value of each candidate accurately. This assessment may be even more complex when types of available information vary from candidate to candidate. Arriving at an optimal project portfolio value requires a uniform assessment that consistently evaluates all alternatives uniformly.
Some investment candidate estimates have been based on identifying existing reservoirs with properties that match or closely match (i.e., are similar to) the new, candidate reservoir. The closest existing reservoirs are known as “analogous reservoirs.”
Typically, one or more experts (e.g., geologists and reservoir engineers) identified and selected analogous reservoirs, based solely on experience and known or available investment candidate properties. Relevant information of such analogous reservoirs are stored in data bases having records with information on volumetric, facies and properties distribution, wherein all records not necessarily have all properties.
The specialized literature shows continuously increased interest in analogues and its predictions. Sophisticated data mining and machine learning algorithms allow estimation of properties and such estimations also define the degree of uncertainty which can be reliably used within geostatistical workflows. Example can be found on the usage of reservoir analogous in the context of reservoir drive mechanism, recovery factor or reserve estimation and classification.
Insufficient information, however, can make selecting analogous reservoirs guesswork at best, and make estimating the value error prone and uncertain. A mis-valuation could lead to wasted resources, e.g., from passing on an undervalued reservoir to exploit an overvalued reservoir. Missing parameters increase uncertainty and the likelihood of a mis-valuation.
Thus, there is a need for generating scenarios compatible with the limited amount of information available on a target hydrocarbon reservoir, while including an associated uncertainty for the assessment; and more particularly, for estimating geological and petrophysical properties.